FactSet Research System
A contrarian investment case in the financial market data provider
FactSet Research Systems, a financial data platform for asset managers and investment researchers, has seen its shares decline by 41% year-to-date. According to the FT, of the 19 analysts, not one single analyst recommends a buy, and two other analysts dropped coverage in the past year.
In summary, its future looks very bleak, and the concerns are quite clear:
Artificial Intelligence threat: There’s a potential risk that some capabilities will be replaced by cheaper AI tools like Gemini and ChatGPT, leaving FactSet with much less value-add for customers.
Limited near-term growth: Over the past 5, 10 and 20 years, FactSet Research has historically grown its net income by 11% annually. However, for FY 2026, FactSet guided a 5% growth, well below its long-term growth trajectory.
Management changes: After 29 years in FactSet and 10 years as the CEO, Phil Snow stepped down, with a first outsider, Sanoke Viswanathan from J.P. Morgan, taking over as the new CEO.
Limited pricing power: Ten years ago, FactSet earned $16,200 per user. Today, it’s averaging $9,800 per user, well below the estimated figures of its larger peers like Bloomberg and S&P Market Intelligence.
Aggressive M&A competitors: The past few years has seen lots of industry consolidation. S&P/IHS Market, Morningstar/Pitchbook, LSE/Refinitiv and Blackrock/Preqin. A more consolidated competitive landscape adds pressure on both FactSet’s pricing power and potential volume growth.
Slowing growth in core customers: FactSet excels among its private equity, investment banking and to a smaller extent, sell-side investment banking customers, with a focus on equities, deals research and portfolio analytics solutions. However, data across customers shows tepid employee growth, especially among graduate and new analyst hires, directly impacting FactSet’s growth potential.
Market cap (As of 10th December 2025): $10.7 billion
Jenga IP 2030 FY estimated market cap: $17.5 billion
Potential IRR (including dividends): 14.9%
Jenga IP Quality Rating: 76.8/100
These six concerns are valid investment risks among others, I view even more critical to FactSet’s long-term potential, such as:
FactSet lacks market leadership and is the 5th largest player behind Bloomberg, LSEG, S&P Global and Moody’s, respectively.
It’s structurally less vertically integrated with less IP content when compared to peers like Bloomberg.
Admittedly, too, I source my financial information in the deep dives and this article from FactSet’s chief competitor, S&P Capital IQ.
Yet, despite my bias towards S&P Global, the various risks mentioned above, and recent share price trends, I still see an investment case in its shares and recently initiated a position for the portfolio. In this report, I’ll walk you through each of these concerns alongside its broader investment case. Here’s what we will cover:
Table of Contents
The Artificial Intelligence threat to financial data research: Insights into the applications of AI tools, LLMs in financial data research, real examples of customer AI use cases and implementation.
History of FactSet: An in-depth overview of FactSet’s history over the past 47 years, initial growth drivers, partnerships and customer relationships, timeline of acquisitions and key events, and a case study review of its performance during the financial crisis.
Global Financial Market Data Industry: An overview of the financial market data research industry, the different segments by users and use cases, the growth of Moody’s Analytics and S&P Market Intelligence, Bloomberg and LSEG leadership, and insights into segments in which FactSet remains competitive.
Customer’s perspective on financial research: Insights from our 20+ interviews with FactSet and its competitors' customers, review of our sales/demo calls with the respective financial data platforms, key highlights on their features, overall performance and competitiveness.
FactSet’s business model: A breakdown of FactSet’s product scope, revenue model, drivers by region, sales model, dynamics per client and user, and insights into the CUSIP Global Services acquisition. Overview of its cost model and structure, and analysis of its unit economics per client and user.
Growth potential: Analysis of its pricing and volume growth drivers. Assessment of the overall market size, penetration rates and drivers of potential user/client growth and an overview of its pricing power and potential.
Risks and challenges: Concluding thoughts on the AI risks, churn rates, limits of its competitive positioning, recent management changes and broader culture, M&A risks and vertical integration.
Valuation: An earnings multiple-led valuation model with full economics projections from FY 2025 to FY 2030. Explanation of our 22x P/E exit multiple and how I arrive at its IRR projections.
Conclusion: Thoughts on the broader Financial information services market decline and other players on my radar.
High quality companies are typically only available at undervalued prices during broad market selloffs or when the market identifies challenges to their fundamentals. Of the nine companies published to date here on Global Outperformers, I believe five qualify as high-quality businesses; two Mexican airports (worries on Mexican government interference), ICTSI (trade wars impact on ports), Alphabet (AI eroding search) and TSMC (China-US trade tensions).
FactSet is on par with these five high quality companies, and the biggest challenge is the potential replacement by various AI tools and apps. Given the key risk here, let’s first begin by exploring the AI threat.
1. The AI threat to financial data research services
Financial exchanges and data companies have historically proven to be among the highest quality businesses. Their services, tools and platforms are at the heart of modern finance. Moody’s and S&P Global are mission-critical to investors and companies via their credit rating solutions. The LSE, Nasdaq and Euronext play key roles in the trading and clearing of equities, ETFs and other financial securities. CME Group, Cboe Group and MarketAxess are similarly mission-critical for their customers operating in the fixed income, commodities and options markets.
Until last week (3rd December 2025), an equally weighted portfolio of 15 leading financial exchange and data companies was justifiably valued at a premium to the S&P 500 as investors rewarded their superior business economics, overall moat and growth prospects.
Year to date, an equally weighted portfolio of the 15 financial exchange and data companies is down -2% versus +16% of the S&P 500, with their forward P/E marginally below the S&P 500 (23.1x), the first time since the first half of 2012.
The key reason for their recent underperformance is the AI threat to their business economics and growth prospects, and here, the dispersion across the companies is quite wide, with the data research providers experiencing the brunt of the YTD decline:
FactSet Research Systems: -40%
Morningstar: -40%
Value Line: -29%
London Stock Exchange Group: -27%
Before diving into FactSet, given the impact of the AI risk, it’s essential to begin our analysis by mapping out the AI threat. As part of my FactSet research, I interviewed over 20 financial industry professionals with experience using these financial market data tools, with each interview revealing key insights into how AI changes the industry.
While these financial data platforms have their own AI tools, as we’ll discuss later in this deep dive, AI-native tools and solutions come in different forms, and we can categorise them into four key areas:
General AI applications: Tools and companies aiming to replace all features present among the incumbents, e.g. Perplexity AI and OpenBB.
Financial analysis: AI tools seeking to support users in a few but not all areas of the investment analysis process, e.g. TIKR, Overbond and Magnifi.
Support tools: AI services supporting one key function of the broader investment workflow, e.g. creating charts (YCharts), conversations with peers (Symphony) and earnings call transcripts (Quartr).
Middle office: AI services dedicated to middle office solutions found within incumbents, such as ESG analytics, Portfolio Management analytics and solutions, among others.
In the table below, I highlight some AI-native startups addressing the categories, and while this is far from a complete list, it does show that the threat from AI is real.
